Deep Vertical Overview: Sentinel is not a generic chatbot. It operates as a specialized analytical agent embedded in each tool page. When you run an analysis, whether it is option pricing, a backtest, a Monte Carlo simulation, or portfolio optimization, Sentinel receives the full computational output along with methodology-specific context. It understands what the Greeks mean in Option Pricing, how to interpret the efficient frontier in Markowitz, what correlation breakdowns imply in Asset Relation analysis, and so on for every tool. This domain awareness enables a deep, vertical commentary rather than a surface-level summary.

Automatic Macro Context Enrichment: Before generating its analysis, Sentinel automatically enriches the context by retrieving macro data related to the concepts linked to the analysis and its calculation methodologies, as well as data related to the asset under review, for example last price, some technical indicators, last and historical volatility, average volume. This adds a market context layer that you do not need to provide manually, allowing Sentinel to ground its commentary in the broader market environment.

Cross-Page Session Memory: Sentinel retains micro-summaries of your previous analyses throughout the session. If you first analyze AAPL's historical volatility, then run a backtest on AAPL, and later open Delta Hedging for the same asset, Sentinel on the third page already has visibility into the findings from your previous two analyses. This progressive accumulation of context produces increasingly complete and coherent insights as you move through different tools.

Intra-Page Data Accumulation: Within a single page, if a tool has multiple output sections, for example, Chart and Correlations in the Indicator page, Sentinel sees all data available at the time of generation. If you load Chart results first and then Correlations data, regenerating Sentinel will incorporate both result sets into its analysis, giving you a unified overview across all sections.